code: def multi_class_mask_decoder_forward(
      self,
      image_embeddings: torch.Tensor,
      image_positional_embeddings: torch.Tensor,
      sparse_prompt_embeddings: torch.Tensor,
      dense_prompt_embeddings: torch.Tensor,
      multimask_output: bool,
      output_attentions: Optional[bool] = None,
      attention_similarity: torch.Tensor = None,
      target_embedding: torch.Tensor = None,
  ) -> Tuple[torch.Tensor, torch.Tensor]:
      """
      Modify the forward method of SamMaskDecoder for multi-class semantic segmentation
      based on https://github.com/huggingface/transformers/blob/main/src/transformers/models/sam/modeling_sam.py#L468.
  
      Args:
          image_embeddings (`torch.Tensor`):
              the embeddings from the image encoder
          image_positional_embedding (`torch.Tensor`):
              positional encoding with the shape of image_embeddings
          sparse_prompt_embeddings (`torch.Tensor`):
              The embeddings of the points and boxes
          dense_prompt_embeddings (`torch.Tensor`):
              the embeddings of the mask inputs
          multimask_output (bool):
              Whether to return multiple masks or a single mask.
          output_attentions (bool, *optional*):
              Whether or not to return the attentions tensors of all attention layers.
      """
      batch_size, num_channels, height, width = image_embeddings.shape
      point_batch_size = sparse_prompt_embeddings.shape[1]
      # Concatenate output tokens
      output_tokens = torch.cat(
          [self.iou_token.weight, self.mask_tokens.weight], dim=0
      )
      output_tokens = output_tokens.repeat(
          batch_size, point_batch_size, 1, 1
      )
  
      if sparse_prompt_embeddings.sum().item() != 0:
          tokens = torch.cat(
              (output_tokens, sparse_prompt_embeddings), dim=2
          )
      else:
          tokens = output_tokens
      point_embeddings = tokens.to(self.iou_token.weight.dtype)
  
      # Expand per-image data in batch direction to be per-point
      image_embeddings = image_embeddings + dense_prompt_embeddings
      image_embeddings = image_embeddings.repeat(
          point_batch_size, 1, 1, 1
      )
      image_positional_embeddings = image_positional_embeddings.repeat(
          point_batch_size, 1, 1, 1
      )
  
      # Run the transformer, image_positional_embedding are consumed
      point_embedding, image_embeddings, attentions = self.transformer(
          point_embeddings=point_embeddings,
          image_embeddings=image_embeddings,
          image_positional_embeddings=image_positional_embeddings,
          attention_similarity=attention_similarity,
          target_embedding=target_embedding,
          output_attentions=output_attentions,
      )
      iou_token_out = point_embedding[:, :, 0, :]
      mask_tokens_out = point_embedding[
          :, :, 1 : (1 + self.num_mask_tokens), :
      ]
  
      # Upscale mask embeddings and predict masks using the mask tokens
      image_embeddings = image_embeddings.transpose(2, 3).reshape(
          batch_size * point_batch_size, num_channels, height, width
      )
  
      upscaled_embedding = self.upscale_conv1(image_embeddings)
      upscaled_embedding = self.activation(
          self.upscale_layer_norm(upscaled_embedding)
      )
      upscaled_embedding = self.activation(
          self.upscale_conv2(upscaled_embedding)
      )
  
      ################ Modify the original code. We aim at returning a single mask for each object.
      ################ Original logic is to return multiple masks.
      ################ So we use an MLP network to process all the mask proposals instead of multiple networks.
      # hyper_in_list = []
      # for i in range(self.num_mask_tokens):
      #     current_mlp = self.output_hypernetworks_mlps[i]
      #     hyper_in_list += [current_mlp(mask_tokens_out[:, :, i, :])]
      # hyper_in = torch.stack(hyper_in_list, dim=2)
      hyper_in = self.output_hypernetworks_mlps[0](mask_tokens_out)
      ################
  
      _, num_channels, height, width = upscaled_embedding.shape
      upscaled_embedding = upscaled_embedding.reshape(
          batch_size, point_batch_size, num_channels, height * width
      )
      masks = (hyper_in @ upscaled_embedding).reshape(
          batch_size, point_batch_size, -1, height, width
      )  # bs, 1, num_tokens, h, w
  
      ################ New added class prediction logic.
      class_predictions = self.output_classifier_mlps(
          mask_tokens_out
      ).reshape(batch_size, point_batch_size, -1, self.num_classes + 1)
      ################
  
      # Generate mask quality predictions
      iou_pred = self.iou_prediction_head(iou_token_out)
  
      outputs = (masks, iou_pred)
  
      if output_attentions:
          outputs = outputs + (attentions,)
      else:
          outputs = outputs + (None,)
  
      return outputs + (class_predictions,)
  
imports: import logging
  import os
  import time
  import warnings
  from typing import Dict, List, Optional, Tuple
  import torch
  import torch.nn.functional
  from omegaconf import DictConfig
  from torch import nn
  from transformers import SamConfig, SamModel
  from transformers.models.sam.modeling_sam import SamImageSegmentationOutput
  from ..constants import CLASS_LABEL, CLASS_LOGITS, COLUMN, IMAGE, IMAGE_VALID_NUM, LABEL, LOGITS, MASK_LABEL
  from .utils import assign_layer_ids, freeze_model_layers
label_desc: Use image node_id 1 for calling a global function with example usage: multi_class_mask_decoder_forward(image_embeddings, image_positional_embeddings, sparse_prompt_embeddings, dense_prompt_embeddings, multimask_output, output_attentions, attention_similarity, target_embedding) and returns: unknown